AN INVESTIGATION OF DIFFERENT VIDEO WATERMARKING TECHNIQUES

Watermarking is an advanced technology that identifies to solve the problem of illegal manipulation and distribution of digital data. It is the art of hiding the copyright information into host such that the embedded data is imperceptible. The covers in the forms of digital multimedia object, namely image, audio and video. The extensive literature collected related to the performance improvement of video watermarking techniques is critically reviewed and presented in this paper. Also, comprehensive review of the literature on the evolution of various video watermarking techniques to achieve robustness and to maintain the quality of watermarked video sequences

Utilização da Norma JPEG2000 para codificar proteger e comercializar Produtos de Observação Terrestre

Applications like, change detection, global monitoring, disaster detection and management have emerging requirements that need the availability of large amounts of data. This data is currently being capture by a multiplicity of instruments and EO (Earth Observation) sensors originating large volumes of data that needs to be stored, processed and accessed in order to be useful – as an example, ENVISAT accumulates, in a yearly basis, several hundred terabytes of data. This need to recover, store, process and access brings some interesting challenges, like storage space, processing power, bandwidth and security, just to mention a few. These challenges are still very important on today’s technological world. If we take a look for example at the number of subscribers of ISP (Internet Service Providers) broadband services on the developed world today, one can notice that broadband services are still far from being common and dominant. On the underdeveloped countries the picture is even dimmer, not only from a bandwidth point of view but also in all other aspects regarding information and communication technologies (ICTs). All this challenges need to be taken into account if a service is to reach the broadest audience possible. Obviously protection and securing of services and contents is an extra asset that helps on the preservation of possible business values, especially if we consider such a costly business as the space industry. This thesis presents and describes a system which allows, not only the encoding and decoding of several EO products into a JPEG2000 format, but also supports some of the security requirements identified previously that allows ESA (European Space Agency) and related EO services to define and apply efficient EO data access security policies and even to exploit new ways to commerce EO products over the Internet.Aplicações como, detecção de mudanças no terreno, monitorização planetária, detecção e gestão de desastres, têm necessidades prementes que necessitam de vastas quantidades de dados. Estes dados estão presentemente a ser capturados por uma multiplicidade de instrumentos e sensores de observação terrestre, que originam uma enormidade de dados que necessitam de ser armazenados processados e acedidos de forma a se tornarem úteis – por exemplo, a ENVISAT acumula anualmente varias centenas de terabytes de dados. Esta necessidade de recuperar, armazenar, processar e aceder introduz alguns desafios interessantes como o espaço de armazenamento, poder de processamento, largura de banda e segurança dos dados só para mencionar alguns. Estes desafios são muito importantes no mundo tecnológico de hoje. Se olharmos, por exemplo, ao número actual de subscritores de ISP (Internet Service Providers) de banda larga nos países desenvolvidos podemos ficar surpreendidos com o facto do número de subscritores desses serviços ainda não ser uma maioria da população ou dos agregados familiares. Nos países subdesenvolvidos o quadro é ainda mais negro não só do ponto de vista da largura de banda mas também de todos os outros aspectos relacionados com Tecnologias da Informação e Comunicação (TICs). Todos estes aspectos devem ser levados em consideração se se pretende que um serviço se torne o mais abrangente possível em termos de audiências. Obviamente a protecção e segurança dos conteúdos é um factor extra que ajuda a preservar possíveis valores de negócio, especialmente considerando industrias tão onerosas como a Industria Espacial. Esta tese apresenta e descreve um sistema que permite, não só a codificação e descodificação de diversos produtos de observação terrestre para formato JPEG2000 mas também o suporte de alguns requisitos de segurança identificados previamente que permitem, á Agência Espacial Europeia e a outros serviços relacionados com observação terrestre, a aplicação de politicas eficientes de acesso seguro a produtos de observação terrestre, permitindo até o aparecimento de novas forma de comercialização de produtos de observação terrestre através da Internet

Delineation of Road Networks from Remote Sensor Data with Deep Learning

In this thesis we address the problem of semantic segmentation in geospatial data. We investigate different deep neural network architectures and present a complete pipeline for extracting road network vector data from satellite RGB orthophotos of urban areas. Firstly, we present a network based on the SegNeXt architecture for the semantic segmentation of the roads. A novel loss function is introduced for training the network. The results show that the proposed network produces on average better results than other state-of-the-art semantic segmentation techniques. Secondly, we propose a fast post-processing technique for vectorizing the rasterized segmentation result, removing erroneous lines, and refining the road network. The result is a set of vectors representing the road network. We have extensively tested the proposed pipeline and provide quantitative comparisons with other state-of-the-art based on a number of known metrics. This work has been published and presented at the 14 th International Symposium on Visual Computing, 2019. Finally, we present an altogether different approach to road extraction. We reformulate the task of extracting vectorized road networks as a deep reinforcement learning problem with partially observable state-space and present our preliminary results and future work

On-line remaining useful life estimation of power connectors focused on predictive maintenance

Connections are critical elements in power systems, exhibiting higher failure probability. Power connectors are considered secondary simple devices in power systems despite their key role, since a failure in one such element can lead to major issues. Thus, it is of vital interest to develop predictive maintenance approaches to minimize these issues. This paper proposes an on-line method to determine the remaining useful life (RUL) of power connectors. It is based on a simple and accurate model of the degradation with time of the electrical resistance of the connector, which only has two parameters, whose values are identified from on-line acquired data (voltage drop across the connector, electric current and temperature). The accuracy of the model presented in this paper is compared with the widely applied autoregressive integrated moving average model (ARIMA), showing enhanced performance. Next, a criterion to determine the RUL is proposed, which is based on the inflection point of the expression describing the electrical resistance degradation. This strategy allows determination of when the connector must be replaced, thus easing predictive maintenance tasks. Experimental results from seven connectors show the potential and viability of the suggested method, which can be applied to many other devices.This research was partially funded by Ministerio de Ciencia, Innovación y Universidades de España, grant number RTC-2017-6297-3 and by the Generalitat de Catalunya, grant number 2017 SGR 967.Peer ReviewedPostprint (author's final draft

Load/Price Forecasting and Management of Demand Response for Smart Grids: Methodologies and Challenges

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Camera-Based Heart Rate Extraction in Noisy Environments

Remote photoplethysmography (rPPG) is a non-invasive technique that benefits from video to measure vital signs such as the heart rate (HR). In rPPG estimation, noise can introduce artifacts that distort rPPG signal and jeopardize accurate HR measurement. Considering that most rPPG studies occurred in lab-controlled environments, the issue of noise in realistic conditions remains open. This thesis aims to examine the challenges of noise in rPPG estimation in realistic scenarios, specifically investigating the effect of noise arising from illumination variation and motion artifacts on the predicted rPPG HR. To mitigate the impact of noise, a modular rPPG measurement framework, comprising data preprocessing, region of interest, signal extraction, preparation, processing, and HR extraction is developed. The proposed pipeline is tested on the LGI-PPGI-Face-Video-Database public dataset, hosting four different candidates and real-life scenarios. In the RoI module, raw rPPG signals were extracted from the dataset using three machine learning-based face detectors, namely Haarcascade, Dlib, and MediaPipe, in parallel. Subsequently, the collected signals underwent preprocessing, independent component analysis, denoising, and frequency domain conversion for peak detection. Overall, the Dlib face detector leads to the most successful HR for the majority of scenarios. In 50% of all scenarios and candidates, the average predicted HR for Dlib is either in line or very close to the average reference HR. The extracted HRs from the Haarcascade and MediaPipe architectures make up 31.25% and 18.75% of plausible results, respectively. The analysis highlighted the importance of fixated facial landmarks in collecting quality raw data and reducing noise

Intelligent synthesis of hyperspectral images from arbitrary web cameras in latent sparse space reconstruction

Synthesizing hyperspectral images (HSI) from an ordinary camera has been accomplished recently. However, such computation models require detailed properties of the target camera, which can only be measured in a professional lab. This prerequisite prevents the synthesizing model from being installed on arbitrary cameras for end-users. This study offers a calibration-free method for transforming any camera into an HSI camera. Our solution requires no controllable light sources and spectrometers. Any consumer installing the program should produce high-quality HSI without the assistance of optical laboratories. Our approach facilitates a cycle-generative adversarial network (cycle-GAN) and sparse assimilation method to render the illumination-dependent spectral response function (SRF) of the underlying camera at the first part of the setup stage. The current illuminating function (CIF) must be identified for each image and decoupled from the underlying model. The HSI model is then integrated with the static SRF and dynamic CIF in the second part of the stage. The estimated SRFs and CIFs have been double-checked with the results by the standard laboratory method. The reconstructed HSIs have errors under 3% in the root mean square